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Google Exam Professional Data Engineer Topic 3 Question 71 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 71
Topic #: 3
[All Professional Data Engineer Questions]

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat

a. What should you do?

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Suggested Answer: C

by Lucy at Oct 09, 2023, 05:21 AM

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Audra
17 days ago
As a video enthusiast, I'd love to see a recommendation engine that can keep up with my binge-watching habits! Option C sounds like it would be a 'reel' winner.
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Stephanie
1 months ago
Hmm, I'm not sure I'd want to build a complex Spark MLlib model just for this task. The Cloud Video Intelligence API in Option C or D looks like a more efficient solution to me.
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Frederic
2 days ago
I agree, Option D also seems like a good option. Storing data in Cloud SQL could be useful.
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Talia
11 days ago
Option C sounds like a good choice. Using the Cloud Video Intelligence API seems efficient.
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Fernanda
1 months ago
Option D seems like a good choice if you're already familiar with Cloud SQL. The simplicity of storing data in a relational database and joining the labels with user history could be a straightforward approach.
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Tonette
23 hours ago
That's true, combining the API for labels with Cloud SQL for filtering could be a powerful combination.
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Rashida
4 days ago
But wouldn't using the Cloud Video Intelligence API to generate labels be more accurate?
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Nina
5 days ago
I agree, using Cloud SQL for storing data and joining labels with user history sounds efficient.
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Kenia
28 days ago
Option D seems like a good choice if you're already familiar with Cloud SQL.
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Joana
2 months ago
I'm leaning towards Option B. Having two separate models, one for generating labels and one for filtering, could provide more flexibility and control over the recommendation process.
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Bernardine
30 days ago
I agree, Option B seems like it would give us more control over the recommendation process.
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Frederica
1 months ago
Option B sounds like a good choice. Having separate models for generating labels and filtering could be beneficial.
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Patrick
2 months ago
That's a good point, but I still think option A is more straightforward and easier to implement.
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Lynelle
2 months ago
I disagree, I believe option B is better. Having two classification models for labeling and filtering can provide more accurate results.
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Galen
2 months ago
Option C looks like the most efficient and scalable solution. Using the Cloud Video Intelligence API and Cloud Bigtable seems like a great way to handle the large amounts of data and provide fast filtering capabilities.
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Mary
13 days ago
Yes, using Cloud Bigtable for storing data and filtering predicted labels based on user viewing history is a smart choice for this recommendation engine application.
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Arthur
17 days ago
It's important to have fast filtering suggestions based on customer preferences, and Cloud Bigtable can definitely help with that.
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Jeffrey
23 days ago
I agree, Cloud Video Intelligence API can help generate accurate labels for the videos, and Cloud Bigtable can handle the massive amount of data efficiently.
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Colene
24 days ago
Option C looks like the most efficient and scalable solution. Using the Cloud Video Intelligence API and Cloud Bigtable seems like a great way to handle the large amounts of data and provide fast filtering capabilities.
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Patrick
2 months ago
I think option A is the best choice. Using Spark MLlib for classification and Cloud Dataproc for deployment seems efficient.
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